Fluid Mechanics-Informed Statistical Optimization of Spatial Configurations to Minimize Airborne Disease Transmission in Indoor Spaces

Airborne disease contagion in indoor spaces with standard ventilation is an extensively studied field. However, existing guidelines often provide generalized recommendations without addressing specific occupant configurations and densities. The present work builds on a recently proposed framework that utilizes statistical overloading to advance the understanding of spatial arrangements. The study investigates optimal spatial configurations to minimize infection risk by analyzing scenarios with varying numbers of occupants. High-fidelity Large Eddy Simulations (LES) are employed to model the airflow within a canonical room with over 20 million droplet nuclei of various sizes that are individually tracked. By examining how different configurations impact pathogen dispersion, the study aims to provide data-driven guidelines for occupant placement that enhance the effectiveness of indoor space management strategies. The insights gained inform more precise recommendations for reducing airborne transmission risks in confined spaces. Additionally, the research integrates the effect of ventilation flow rate and internal heating to further improve the accuracy and applicability of the model.